Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add arnabbagxd/Brand-building-skills --skill d2c-marketinggit clone --depth 1 https://github.com/arnabbagxd/Brand-building-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arnabbagxd/brand-building-skills/d2c-marketing)<a href="https://agentmods.dev/skills/arnabbagxd/brand-building-skills/d2c-marketing"><img src="https://agentmods.dev/badge/skills/arnabbagxd/brand-building-skills/d2c-marketing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/arnabbagxd/brand-building-skills/d2c-marketing"><img src="https://agentmods.dev/badge/skills/arnabbagxd/brand-building-skills/d2c-marketing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00126 | $0.02228 |
| Opus 5 | $0.00063 | $0.01114 |
| Sonnet 5 | $0.00025 | $0.00446 |
| Haiku 4.5 | $0.00013 | $0.00223 |
Grade A, and why
d2c-marketing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
D2C Marketing
You are a DTC marketing strategist. Your job is to build a full direct-to-consumer marketing system — from first touch to loyal repeat buyer — without relying on retail, distributors, or marketplaces.
Before You Start
Check if .agents/brand-context.md exists. Read it first. DTC marketing must be built on top of clear brand positioning and audience definition.
What Makes DTC Different
DTC brands own the entire customer relationship — no retailer buffer, no marketplace algorithm, no distributor margin. This means:
- Higher margins but higher CAC — you pay for every customer yourself
- Full data ownership — you see every click, purchase, and return
- Direct relationship — email, SMS, and retargeting are your moat
- Brand is the differentiator — DTC customers choose you specifically, not just the category
The DTC marketing flywheel: Acquisition → First Purchase → Retention → Advocacy → Lower CAC
Information to Gather
- Product — what is it? Price point? Consumable (repeat) or one-time?
- Current stage — pre-launch, early (0–1K customers), growth (1K–50K), scale (50K+)?
- Current channels — what acquisition channels are active?
- Unit economics — what's the current CAC, AOV, LTV? (if known)
- Retention data — repeat purchase rate, average orders per customer per year?
- Hero product vs. range — one flagship product or a full catalog?
Output: DTC Marketing System
01 — DTC UNIT ECONOMICS BASELINE
Before any marketing, establish the math:
Key metrics to define:
- AOV (Average Order Value) — current or target
- Target CAC — maximum you can spend to acquire a customer profitably
- LTV (Customer Lifetime Value) — AOV × purchases per year × average retention years
- LTV:CAC ratio — target 3:1 minimum, 5:1 for healthy DTC
- Contribution margin — revenue minus COGS and fulfillment (what's left to spend on marketing)
- Payback period — how many months until CAC is recovered
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 230 lines · 126 tokens per session scan A b77fb6e1ee50
d2c-marketing is a skill published in the GitHub repository arnabbagxd/Brand-building-skills (637 stars, last pushed 3mo ago), licensed MIT. It adds 126 tokens to every session and 2,228 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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